Control method and device for mobile platform, and computer readable storage medium

ABSTRACT

A control method for a mobile platform includes obtaining a captured image, determining a target first characteristic part of a target object from the captured image, determining a second characteristic part of the target object in the captured image, and switching from tracking the second characteristic part to tracking the target first characteristic part in response to a tracking parameter of the target object meeting a preset tracking condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/935,709, filed on Jul. 22, 2019, which is continuation ofInternational Application No. PCT/CN2018/073769, filed on Jan. 23, 2018,the entire contents of both of which are incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates to electronic technique and, moreparticularly, to a control method and device for mobile platform, andcomputer readable storage medium.

BACKGROUND

Existing tracking strategy is to track a characteristic part havingobvious characteristics of a target object. Usually during the trackingof a fixed characteristic part of the target object, as the distancebetween a mobile platform and the target object changes, the sizeproportion of a tracking frame of the target object's characteristicpart in the captured image also changes accordingly, which leads to apoor quality performance of the tracking.

For example, when the distance between the mobile platform and thetarget object is very short, the size of the tracking frame at thecharacteristic part in the captured image is relatively large, whichwill cause the tracking speed to slow down and thus easily result in theloss of tracking. On the other hand, when the distance between themobile platform and the target object is relatively long, the size ofthe tracking frame of the characteristic part in the captured image issmall, which will lead to a blurred tracking feature with less usefulinformation. Both of the two situations mentioned above will lower thereliability of the tracking.

SUMMARY

In accordance with the disclosure, there is provided a control methodfor a mobile platform including obtaining a captured image, identifyingone or more candidate first characteristic parts from the capturedimage, determining a second characteristic part of a target object inthe captured image, determining one or more matching parameters eachcorresponding to one of the one or more candidate first characteristicparts based on the one or more candidate first characteristic parts andthe second characteristic part, determining a target firstcharacteristic part of the target object from the one or more candidatefirst characteristic parts based on the one or more matching parameters,and switching from tracking the second characteristic part to trackingthe target first characteristic part in response to a tracking parameterof the target object meeting a preset tracking condition.

Also in accordance with the disclosure, there is provide a mobileplatform including a memory storing a computer program and a processorconfigured to execute the computer program to obtain a captured image,identify one or more candidate first characteristic parts from thecaptured image, determine a second characteristic part of a targetobject in the captured image, determine one or more matching parameterseach corresponding to one of the one or more candidate firstcharacteristic parts based on the one or more candidate firstcharacteristic parts and the second characteristic part, determine atarget first characteristic part of the target object from the one ormore candidate first characteristic parts based on the one or morematching parameters, and switch from tracking the second characteristicpart to tracking the target first characteristic part in response to atracking parameter of the target object meeting a preset trackingcondition.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic flow chart of a control method of a mobileplatform according to an example embodiment.

FIG. 2 is a schematic diagram showing switching from tracking a humanbody of a target object to tracking heads and shoulders of the targetobject when switching from a far-field tracking to a near-field trackingaccording to an example embodiment.

FIG. 3 is a schematic diagram showing switching from tracking heads andshoulders of a target object to tracking a human body of the targetobject when switching from a near-field tracking to a far-field trackingaccording to another example embodiment.

FIG. 4 is a schematic diagram showing an estimation of the heads andshoulders based on a preset proportional relationship and the human bodyof the target object when switching from the far-field tracking to thenear-field tracking according to an example embodiment.

FIG. 5 is a schematic diagram showing an estimation of the heads andshoulders based on a tracking joint of the target object when switchingfrom the far-field tracking to the near-field tracking according to anexample embodiment.

FIG. 6 is a schematic diagram showing an estimation of the human bodybased on a preset proportional relationship and the tracking heads andshoulders of the target object when switching from the near-fieldtracking to the far-field tracking according to an example embodiment.

FIG. 7 is a schematic diagram showing an estimation of the human bodybased on a tracking joint of the target object when switching from thenear-field tracking to the far-field tracking according to an exampleembodiment.

FIG. 8 is a schematic structural diagram of a mobile platform accordingto an example embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Technical solutions of the present disclosure will be described withreference to the drawings. It will be appreciated that the describedembodiments are some rather than all of the embodiments of the presentdisclosure. Other embodiments conceived by those having ordinary skillsin the art on the basis of the described embodiments without inventiveefforts should fall within the scope of the present disclosure. Further,in the case of no conflict, the following embodiments and features ofthe embodiments can be combined with each other.

Terms used in the specification of the present disclosure are intendedto describe example embodiments, instead of limiting the presentdisclosure. The singular forms “a,” “the,” and “this” as used in thepresent disclosure and claims are intended to also include the pluralforms, unless the context clearly indicates otherwise. The term “and/or”used herein includes any suitable combination of one or more relateditems listed.

Although the terms “first,” “second,” and “third,” etc. may be used inthe present disclosure to describe various information, the informationshould not be limited to these terms. These terms are used todistinguish the same type of information from each other. For example,the first information may also be referred to as the second informationwithout departing from the scope of the present disclosure, and viceversa. Depending on context. the terms “if” can be interpreted as “at,”“when,” or “in response to.”

In the embodiments of the present disclosure, a control method of mobileplatform is provided. The mobile platform may include, but is notlimited to, an unmanned aerial vehicle, a ground robot (e.g., anunmanned vehicle, etc.). In some embodiments, the mobile platform caninclude an image capturing device and can capture images through theimage capturing device. In some embodiments, the mobile platform caninclude a gimbal, which can carry the image capturing device, such as acamera, to stabilize and/or adjust the image capturing device.

In the embodiments of the present disclosure, an unmanned aerial vehicle(UAV) is used as an example of the mobile platform for descriptivepurposes, and all the terms “unmanned aerial vehicle” (or “UAV”) can bereplaced with “mobile platform.”

In the conventional technologies, a single characteristic part of atarget object is tracked. For example, a human body or a preset part ofthe human body (e.g., head) of the target object is used as the trackingtarget. In this disclosure, a human body of the target object is alsoreferred to as a “target human body.” However, during the tracking ofthe single characteristic part of the target object, like the distancebetween the UAV and the target object changes, the size proportion ofthe tracking frame of the target object's characteristic part in thecaptured image also changes accordingly, which leads to a poor trackingperformance.

For example, when the distance between the UAV and the target object isvery short, the size of the tracking frame at the characteristic part inthe captured image is relatively large, which will cause the tracking toslow down and thus easily result in the loss of tracking. On the otherhand, when the distance between the UAV and the target object isrelatively long, the size of the tracking frame of the characteristicpart in the captured image is small, which will lead to a blurredtracking feature with less useful information. Both of the twosituations mentioned above will lower the reliability of the tracking ofthe UAV.

In the embodiments of the present disclosure, when the distance betweenthe UAV and the target object is relatively short, the preset part ofthe human body of the target object is used as a tracking target, thatis, the UAV tracks the preset part of the human body of the targetobject. When the distance between the UAV and the target object isrelatively long, the human body of the target object is used as atracking target, that is, the UAV tracks the human body of the targetobject. This method can achieve high quality tracking results. The UAV'stracking process of the target object includes four scenarios, asdescribed below:

Scenario I: near-field tracking. When the distance between the UAV andthe target object is relatively short, the method of near-field trackingcan be used. That is, the preset part (e.g., head, or head and shoulder(also referred to as head-shoulder part), etc.) of the human body of thetarget object can be used as the tracking target and the preset part ofthe human body of the target object can be tracked.

Scenario II: far-field tracking. When the distance between the UAV andthe target object is relatively long, the method of far-field trackingcan be used. That is, the human body of the target object can be used asthe tracking target and the human body of the target object can betracked.

Scenario III: switch from far-field tracking to near-field tracking. Thedistance between the UAV and the target object is relatively long atfirst, and the UAV uses far-field tracking method to track the humanbody of the target object. The distance between the UAV and the targetobject then starts to become shorter and shorter, and when the distanceis shorter than or equal to a distance threshold, a switch fromfar-field tracking to near-field tracking will be triggered. That is,the UAV no longer tracks the human body of the target object, but startsto track the preset part of the human body of the target object.

Scenario IV: switch from near-field tracking to far-field tracking. Thedistance between the UAV and the target object is relatively short atfirst, and the UAV uses near-field tracking method to track the presetpart of the human body of the target object. The distance between theUAV and the target object then starts to become longer and longer, andwhen the distance is longer than or equal to a distance threshold, aswitch from near-field tracking to far-field tracking will be triggered.That is, the UAV no longer tracks the preset part of the human body ofthe target object, but starts to track the human body of the targetobject.

The process of switching from far-field tracking to near-field trackingand the process of switching from near-field tracking to far-fieldtracking are described below in connection with specific embodiments.FIG. 1 is a schematic flow chart of an example control method of amobile platform consistent with the disclosure. The method can beexecuted by the mobile platform, e.g., by one or more processors of themobile platform. Each of the one or more processors can be ageneral-purpose or special-purpose processor.

As shown in FIG. 1, at 101, a captured image is obtained. As describedabove, the mobile platform can include an image capturing deviceconfigured to capture images of the target object while the mobileplatform is tracking the target object. The processor of the mobileplatform can obtain the captured images. The target object refers to anobject being tracked by the mobile platform.

At 102, one or more first characteristic parts (also referred to as“candidate first characteristic parts”) are identified from the capturedimage. Specifically, the processor of the mobile platform can identifythe first characteristic part from the captured image. In someembodiments, a neural network (e.g., a convolutional neural network) canbe used to identify the one or more first characteristic parts from thecaptured image. In some embodiments, after the neural network detectsthe one or more first characteristic parts in the captured image, theneural network can record and return a position and a correspondingimage area of each of the one or more first characteristic parts in thecaptured image. In some embodiments, the position in the captured imageand the corresponding image area of a first characteristic part can berepresented by a detection frame, i.e., a first characteristic part canbe represented by a detection frame.

At 103, a second characteristic part of a target object in the capturedimage is determined. Specifically, after obtaining the captured image,the processor of the mobile platform can determine the secondcharacteristic part in the captured image. In some embodiments, atracking algorithm can be used to determine the second characteristicpart in the captured image. According to the tracking algorithm, when acurrent frame of captured image is obtained, a target area is firstdetermined according to a position of the second characteristic part ofthe target object in a previous frame of captured image. Then, an imagearea that best matches the second characteristic part of the targetobject in the previous frame of captured image is searched for in thetarget area in the current frame of captured image, and the bestmatching image area is determined as the second characteristic part ofthe target object in the current frame of captured image. In someembodiments, the second characteristic part of the target object in thecurrent frame of captured image can be represented by a detection frame.

At 104, one or more matching parameters corresponding to the one or morefirst characteristic parts are determined based on the one or more firstcharacteristic parts and the second characteristic part. In someembodiments, a plurality of first characteristic parts are identified,which include the characteristic part of the target object. A trackingswitch from the second characteristic part of the target object to thefirst characteristic part of the target object is needed when switchingbetween near-field tracking and far-field tracking. Therefore, it isneeded to determine which one of the first characteristic parts in thecaptured image is the first characteristic part of the target object.

In some embodiments, to determine the first characteristic part of thetarget object, the one or more matching parameters corresponding to theone or more first characteristic parts can be determined according tothe second characteristic part of the target object and the one or morefirst characteristic parts. Each of the one or more first characteristicparts can correspond to one matching parameter. A matching parameter canrepresent a possibility of a corresponding first characteristic partbeing the first characteristic part of the target object. In someembodiments, a matching parameter can include at least one of acoincidence degree matching parameter (or simply “coincidence degree”),an image similarity degree matching parameter (or simply “imagesimilarity degree”), or a geometric similarity degree matching parameter(or simply “geometric similarity degree”). That is, the matchingparameter can be any one of the coincidence degree matching parameter,the image similarity degree matching parameter, and the geometricsimilarity degree matching parameter, or be determined based on two ormore of the coincidence degree matching parameter, the image similaritydegree matching parameter, and the geometric similarity degree matchingparameter.

At 105, the first characteristic part of the target object (alsoreferred to as “target first characteristic part”) is determined fromthe one or more first characteristic part based on the one or morematching parameters corresponding to the one or more firstcharacteristic parts. Specifically, as described above, multiple firstcharacteristic parts may be identified from the captured image, wherethe multiple first characteristic parts include the first characteristicpart of the target object. The matching parameters corresponding to thefirst characteristic parts can be used to determine which firstcharacteristic part is the first characteristic part of the targetobject. In some embodiments, determining the first characteristic partof the target object from the one or more first characteristic partsbased on the one or more matching parameters corresponding to the one ormore first characteristic parts includes determining a largest one ofthe one or more matching parameters and determining the firstcharacteristic part corresponding to the largest matching parameter asthe first characteristic part of the target object. Specifically, thelarger the matching parameter value, the more possible it may be for thecorresponding first characteristic part to be that of the target object.Therefore, the first characteristic part corresponding to the largestone of the one or more matching parameters can be determined to be thefirst characteristic part of the target object.

At 106, tracking the second characteristic part of the target object isswitched to tracking the first characteristic part of the target objectif tracking parameters of the target object meet a preset trackingcondition. The tracking parameters of the target object can include thesize proportion of the tracking frame in the captured image and/or thedistance between the target object and the mobile platform. When thetracking parameters do not meet the preset tracking condition, themobile platform tracks the target object by tracking the secondcharacteristic part of the target object. However, when the trackingparameters meet the preset tracking condition, keeping tracking thesecond characteristic part of the target object may lead to poortracking reliability. At this time, switching to track the firstcharacteristic part that is compatible with the current trackingparameters of the target object can be implemented.

In some embodiments where the tracking is switched from a far-fieldtracking to a near-field tracking, the first characteristic part can bea preset part of the human body and the second characteristic part isthe human body. If the tracking parameters of the target object meet asecond preset tracking condition, the tracking will switch from trackingthe second characteristic part of the target object to tracking thefirst characteristic part of the target object. Specifically, before thesecond preset tracking condition is met by the tracking parameters ofthe target object, the mobile platform uses the far-field trackingstrategy to track the second characteristic part of the target object.Once it is confirmed that the tracking parameters of the target objectsatisfy the second preset tracking condition, the mobile platform willswitch to the near-field tracking strategy, i.e., switching fromtracking the second characteristic part of the target object to trackingthe first characteristic part of the target object.

In some embodiments, the tracking parameters of the target objectmeeting the second preset tracking condition can include, but is notlimited to, the size proportion of the tracking frame of the targetobject in the captured image being greater than or equal to a presetsecond proportion threshold and/or the distance between the targetobject and the mobile platform being shorter than or equal to a presetsecond distance. The size proportion being greater than or equal to thepreset second proportion threshold and/or the distance between thetarget object and the mobile platform being shorter than or equal to thepreset second distance can indicate that the distance between the mobileplatform and the target object is relatively short, and hence thetracking strategy can be switched from far-field tracking to near-fieldtracking, that is, switching from the tracking of the human body of thetarget object to the tracking of the preset part of the human body ofthe target object. The preset second proportion threshold and the presetsecond distance can be configured according to experience, which are notlimited here.

For example, as shown in FIG. 2, when the tracking parameters of thetarget object do not meet the second preset tracking condition, themobile platform tracks the human body of the target object. The capturedimage includes a human body 211 of the target object, and head-shoulderparts 201, 202, and 203. In this disclosure, such head-shoulder partsare also referred to as “candidate head-shoulder parts.” The matchingparameter for each of the head-shoulder part can be determined using themethod described above. According to the matching parameters, thehead-shoulder part 201 can be determined to be the head-shoulder part ofthe target object, and can also be referred to as a “targethead-shoulder part.” When the tracking parameters meet the second presettracking condition, the tracking strategy switches to tracking thehead-shoulder part 201 of the target object.

In some embodiments where the tracking is switched from a near-fieldtracking to a far-field tracking, the first characteristic part can be ahuman body and the second characteristic part is a preset part of thehuman body. If the tracking parameters of the target object meet a firstpreset tracking condition, the tracking will switch from tracking thesecond characteristic part of the target object to tracking the firstcharacteristic part of the target object. Specifically, before the firstpreset tracking condition is met by the tracking parameters of thetarget object, the mobile platform uses the near-field tracking strategyto track the second characteristic part of the target object. Once it isconfirmed that the tracking parameters of the target object satisfy thefirst preset tracking condition, the mobile platform will switch to thefar-field tracking strategy, i.e., switching from tracking the secondcharacteristic part of the target object to tracking the firstcharacteristic part of the target object.

In some embodiments, the tracking parameters of the target objectmeeting the first preset tracking condition can include, but is notlimited to, the size proportion of the tracking frame of the targetobject in the captured image being smaller than or equal to a presetfirst proportion threshold and/or the distance between the target objectand the mobile platform being longer than or equal to a preset firstdistance. The size proportion being smaller than or equal to the presetfirst proportion threshold and/or the distance between the target objectand the mobile platform being longer than or equal to the preset firstdistance can indicate that the distance between the mobile platform andthe target object is relatively long, and hence the tracking strategycan be switched from near-field tracking to far-field tracking, that is,switching from the tracking of the preset part of the human body of thetarget object to the tracking of the human body of the target object.The preset first proportion threshold and the preset first distance canbe configured according to experience, which are not limited here.

For example, as shown in FIG. 3, when the tracking parameters of thetarget object do not meet the first preset tracking condition, themobile platform tracks the head-shoulder part of the target object. Thecaptured image includes a head-shoulder part 231 of the target object,and human bodies 221, 222, and 223. In this disclosure, such humanbodies are also referred to as “candidate human bodies.” The matchingparameters for each of the human body can be determined using the methoddescribed above. According to the matching parameters, the human body221 can be determined to be the human body of the target object, i.e.,the human body 221 can be determined to be the target human body. Whenthe tracking parameters meet the first preset tracking condition, thetracking strategy switches to tracking the human body 221 of the targetobject.

In some embodiments, determining the matching parameters correspondingto the first characteristic parts based on the second characteristicpart of the target object and the one or more first characteristic partscan include determining the matching parameters between the secondcharacteristic part of the target object and the first characteristicparts based on the second characteristic part of the target object andthe first characteristic parts. Specifically, the matching parametersbetween the second characteristic part of the target object and aparticular first characteristic part can represent the degree ofmatching between the second characteristic part of the target object andthat particular first characteristic part. The higher the degree ofmatching, the more possible it may be for the particular firstcharacteristic part to be the first characteristic part of the targetobject.

As shown in FIG. 2, the tracking is switched from a far-field trackingto a near-field tracking. The matching parameter between the human body211 of the target object and the head-shoulder part 201 can bedetermined according to the human body 211 of the target object and thehead-shoulder part 201. The matching parameter between the human body211 of the target object and the head-shoulder part 202 can bedetermined according to the human body 211 of the target object and thehead-shoulder part 202. The matching parameter between the human body211 of the target object and the head-shoulder part 203 can bedetermined according to the human body 211 of the target object and thehead-shoulder part 203.

As described above, the matching parameter can be any one of thecoincidence degree matching parameter, the image similarity degreematching parameter, and the geometric similarity degree matchingparameter.

In some embodiments, the coincidence degree matching parameter betweenthe human body 211 of the target object and the head-shoulder part 201can be determined based on the human body 211 of the target object andthe head-shoulder part 201. The coincidence degree matching parameter isused to indicate the degree of coincidence of two image regions and canbe represented by the ratio of the intersection to the union of the twoimage regions. In this embodiment, the coincidence degree matchingparameter between the human body 211 of the target object and thehead-shoulder part 201 can be determined by calculating the ratio of theintersection to the union of the region of the human body 211 of thetarget object and head-shoulder part 201. The higher the coincidencedegree matching parameter, the more possible it may be for thehead-shoulder part 201 to be the head-shoulder part of the targetobject.

In some embodiments, the image similarity matching parameter between thehuman body 211 of the target object and the head-shoulder part 201 canbe determined based on the human body 211 of the target object and thehead-shoulder part 201. The image similarity matching parameter is usedto indicate the degree of similarity between images in two image regionsand can be determined using histograms of the images in the two imageregions. In this embodiment, the histograms of the human body 211 of thetarget object and the head-shoulder part 201 are determined,respectively. Then a normalized correlation coefficients (e.g., Barrdistance, histogram intersection distance, etc.) between the twohistograms can be calculated as the image similarity matching parameter.While the normalized correlation coefficient method is an example, thoseskills in the art may use other methods to determine the imagesimilarity degree matching parameter, which is not specifically limitedhere. The degree of similarity between the human body 211 of the targetobject and the head-shoulder part 201 can be determined according to theimage similarity matching parameter between the human body 211 of thetarget object and the head-shoulder part 201. The higher the imagesimilarity matching parameter, the more possible it may be for thehead-shoulder part 201 to be the head-shoulder part of the targetobject.

In some embodiments, the geometric similarity matching parameter betweenthe human body 211 of the target object and the head-shoulder part 201can be determined based on the human body 211 of the target object andthe head-shoulder part 201. The geometric similarity matching parameteris used to indicate the size matching degree of two image regions.Usually in consecutive image frames, the motion of the target objectdoes not change much, and the change of the distance between the targetobject and the mobile platform is small. Therefore, the proportionalrelationship of the size between the characteristic parts of the targetobject in an image typically exhibits a preset ratio. In thisembodiment, the area ratio of the human body 211 of the target object tothe head-shoulder part 201 can be calculated according to the area ofthe human body 211 of the target object and the area of thehead-shoulder part 201, and then compared to the preset ratio todetermine a difference. In this embodiment, the preset ratio can be thesize ratio between the human body and the head-shoulder part. The ratiodifference can be used to determine the geometric similarity matchingparameter. The degree of size matching between the human body 211 of thetarget object and the head-shoulder part 201 can be determined accordingto the geometric similarity matching parameter between the human body211 of the target object and the head-shoulder part 201. The higher thesize matching degree, the more possible it may be for the head-shoulderpart 201 to be the head-shoulder part of the target object.

In some embodiments, the matching parameter between the human body 211and the head-shoulder part 201 can include at least one of thecoincidence degree matching parameter, the image similarity degreematching parameter, or the geometric similarity degree matchingparameter. That is, those skills in the art may use any one of thecoincidence degree matching parameter, the image similarity degreematching parameter, and the geometric similarity degree matchingparameter as the matching parameter between the human body 211 and thehead-shoulder part 201, or fuse two or more of the coincidence degreematching parameter, the image similarity degree matching parameter, andthe geometric similarity degree matching parameter to determine thematching parameter between the human body 211 and the head-shoulder part201, which is not specifically limited here. With the above describedmethod, the matching parameter between the human body 211 and thehead-shoulder part 202, and the matching parameter between the humanbody 211 and the head-shoulder part 203 can also be determined.

In some embodiments, as shown in FIG. 3, the tracking is switched from anear-field tracking to a far-field tracking. The matching parameterbetween the second characteristic part 231 and the first characteristicpart 221 can be determined according to the second characteristic part231 of the target object and the first characteristic part 221. Thematching parameter between the second characteristic part 231 and thefirst characteristic part 222 can be determined according to the secondcharacteristic part 231 of the target object and the firstcharacteristic part 222. The matching parameter between the secondcharacteristic part 231 and the first characteristic part 223 can bedetermined according to the second characteristic part 231 of the targetobject and the first characteristic part 223. For the specific method ofdetermining the matching parameter between the second characteristicpart 231 and the first characteristic part 221, the matching parameterbetween the second characteristic part 231 and the first characteristicpart 222, and the matching parameter between the second characteristicpart 231 and the first characteristic part 223, reference can be made tothe description above.

In some other embodiments, a third characteristic part can be estimated.The third characteristic part is an estimated first characteristic partof the target object estimated according to the second characteristicpart of the target object. Then the matching parameter between the thirdcharacteristic part and the one or more first characteristic parts canbe determined.

The estimation of the third characteristic part of the target object caninclude estimating the third characteristic part according to a presetproportional relationship and the second characteristic part of thetarget object, or estimating the third characteristic part according toone or more joint points of the target object determined by the secondcharacteristic part of the target object. The preset proportionalrelationship can refer to, e.g., the ratio of the first characteristicpart to the second characteristic part of a person, which can be anexperience value.

As shown in FIG. 4, the tracking is switched from a far-field trackingto a near-field tracking. A third characteristic part 212, i.e., ahead-shoulder part 212, of the target object can be estimated accordingto the preset proportional relationship and the human body 211 of thetarget object, where the preset proportional relationship refers to theratio of the human body to the head-shoulder part of the human body.

As shown in FIG. 5, the tracking is switched from a far-field trackingto a near-field tracking. The joint points of the target object can bedetermined according to the human body 211, and a head-shoulder part 213can be estimated according to the joint points of the target object. Forexample, the joints of shoulders and the joints of eyes of the targetobject are determined and used to estimate the head-shoulder part 213 ofthe target object.

After the estimated head-shoulder part 213 is obtained, the matchingparameter between the estimated head-shoulder part 213 and thehead-shoulder part 201 can be determined according to the estimatedhead-shoulder part 213 and the head-shoulder part 201. The matchingparameter between the estimated head-shoulder part 213 and thehead-shoulder part 202 can be determined according to the estimatedhead-shoulder part 213 and the head-shoulder part 202. The matchingparameter between the estimated head-shoulder part 213 and thehead-shoulder part 203 can be determined according to the estimatedhead-shoulder part 213 and the head-shoulder part 203. For the specificmethod of determining the matching parameter, reference can be made tothe description above.

As shown in FIG. 6, the tracking is switched from a near-field trackingto a far-field tracking. The human body 232 can be estimated accordingto the preset proportional relationship and the head-shoulder part 231of the target object, where the preset proportional relationship refersto the ratio of the human body to the head-shoulder part.

As shown in FIG. 7, the tracking is switched from a far-field trackingto a near-field tracking. The joint point of the target object can bedetermined according to the head-shoulder part of the target object 231,and the human body 233 can be estimated according to the joint point ofthe target object. For example, the joints of feet, the joints ofshoulders and the joints of eyes of the target object are determined andused to estimate the human body 233 of the target object.

After the estimated human body 233 is obtained, the matching parameterbetween the estimated human body 233 and the human body 221 can bedetermined according to the estimated human body 233 and the human body221. The matching parameter between the estimated human body 233 and thehuman body 222 can be determined according to the estimated human body233 and the human body 222. The matching parameter between the estimatedhuman body 233 and the human body 223 can be determined according to theestimated human body 233 and the human body 223. For the specific methodof determining the matching parameter, reference can be made to thedescription above.

In the above embodiments, if there is an overlap of two or more objectsin the image, the distance between the top edge of the tracking frame ofthe first characteristic part corresponding to the largest matchingparameter and the top edge of the tracking frame of the secondcharacteristic part can be determined. Specifically, the firstcharacteristic part corresponding to the largest matching parameter isdetermined to be the first characteristic part of the target object. Ifthe distance is less than or equal to a preset distance threshold (whichis usually configured according to experience, and is not restrictedhere), the first characteristic part corresponding to the largestmatching parameter can be considered as the first characteristic part ofthe target object. If the distance is larger than the preset distancethreshold, then the first characteristic part corresponding to thelargest matching parameter is eliminated, and a new largest matchingparameter will be determined from the remaining ones of the matchingparameters, and so on.

For example, in FIG. 2, if the distance between the top edges of thetracking frames of the first characteristic part 201 corresponding tothe largest matching parameter and the second characteristic part 211 isless than or equal to the preset distance threshold, the firstcharacteristic part 201 is identified as the first characteristic partof the target object. If the distance is larger than the preset distancethreshold, the first characteristic part 201 is eliminated, and thefirst characteristic part corresponding to a new largest matchingparameter can be determined between the first characteristic part 202and the first characteristic part 203, and so on.

In the above embodiments, the far-field tracking strategy tracks thehuman body of the target object. When the mobile platform approaches thetarget object, the size of the tracking frame at the human body in thecaptured image becomes large, which will cause the tracking speed toslow down. By switching from the far-field tracking to the near-fieldtracking that tracks the head-shoulder part of the target object, thetracking efficiency can be improved. On the other hand, the near-fieldtracking strategy tracks the head-shoulder part of the target object.When the mobile platform moves away from the target object, the size ofthe tracking frame at the head-shoulder part in the captured imagebecomes small, which reduces the tracking accuracy. By switching fromthe near-field tracking to the far-field tracking that tracks the humanbody of the target object, the tracking can be more accurate.

Since the size of the tracking frame may change to some extent, the sizeof the tracking frame may not be consistent with the size of the trackedtarget object. Some other auxiliary methods can be used to determine theswitching condition to improve the switching accuracy. For example, adepth sensor detection technology combined with the image projectionrelationship can be used to obtain the depth of the target object in thecaptured image, hence get the distance between the mobile platform andthe target object. If the distance is too short (for example, shorterthan or equal to 3 meters), it switches to near-field tracking, if it istoo long (for example, longer than 4 meters), it switches to far-fieldtracking. In some other embodiments, the distance between the mobileplatform and the target object can be also obtained with direct rangingmethod, e.g., binocular ranging, ultrasonic ranging, and lidar ranging,etc.

In an example, the application scenarios applicable to the foregoingembodiments may include but are not limited to, e.g., selfie mode,taking off with a face ID, taking video from near to far, and/or takingsurround video from near to far.

Selfie mode. If the target object is a person, the UAV measures the sizeof the tracking frame when the target object is detected. If the sizeratio of the tracking frame in the captured image is greater than orequal to 30%, the detection of the head-shoulder part of the targetobject is automatically implemented, that is, the near-field tracking isperformed. When the target object is relatively far away from the UAVand the size ratio of the tracking frame in the captured image issmaller than 10%, the tracking switches to the far-field to detect thehuman body of the target object automatically.

Taking off with a face ID. A UAV with a face ID function can start withthe near-field tracking (e.g., the head-shoulder part tracking) after asuccessful face scan. When the target object moves to far away from theUAV, the UAV switches to the far-field tracking (e.g., the human bodytracking). This function enables an automatic focus on the useractivating the UAV.

Taking video from near to far. The UAV can take off directly from theuser's hands, fly obliquely upward and backward, and start to track thehead-shoulder part of the target object. Once the UAV flies out andreaches to a relatively long distance away from the target object, itmay switch to the far-field tracking to track the human body of thetarget object.

Taking surround video from near to far. The UAV spirals out to recordvideo after focusing on the human body. Specifically, the UAV can takeoff directly from the user's hands, and start the spiral flight shootingand tracking of the head-shoulder part of the target object. Once theUAV flies out and reaches to a relatively long distance away from thetarget object, it may switch to the far-field tracking to track thehuman body of the target object.

As shown in FIG. 8, an embodiment of the present disclosure furtherprovides a mobile platform 30 including a memory 31 and a processor 32(one or more processors).

In one embodiment, the memory is configured to store a computer programand the processor is configured to execute the computer program toobtain a captured image, identify one or more first characteristic partsfrom the captured image, determine a second characteristic part of atarget object in the captured image, determine one or more matchingparameters corresponding to the one or more first characteristic partsbased on the one or more first characteristic parts and the secondcharacteristic part, determining the first characteristic part of thetarget object from the one or more first characteristic parts based onthe one or more matching parameters corresponding to the one or morefirst characteristic parts, and switch from tracking the secondcharacteristic part of the target object to tracking the firstcharacteristic part of the target object if tracking parameters of thetarget object meet a preset tracking condition.

In some embodiments, the processor is further configured to, whendetermining the one or more matching parameters corresponding to the oneor more first characteristic parts based on the second characteristicpart of the target object and the one or more first characteristicparts, determine the one or more matching parameters between the secondcharacteristic part of the target object and the one or more firstcharacteristic parts based on the second characteristic part of thetarget object and the one or more first characteristic parts.

In some embodiments, the processor is further configured to, whendetermining one or more matching parameters corresponding to the one ormore first characteristic parts based on the second characteristic partof the target object and the one or more first characteristic parts,estimate a third characteristic part of the target object. Specifically,the third characteristic part is an estimated first characteristic partof the target object estimated according to the second characteristicpart of the target object. Then the matching parameter between the thirdcharacteristic part of the target object and the one or more firstcharacteristic parts can be determined according to the thirdcharacteristic part of the target object and the one or more firstcharacteristic parts.

In some embodiments, the processor is further configured to, whenestimating the third characteristic part of the target object, estimatethe third characteristic part of the target object based on theproportional relationship between the second characteristic part of thetarget object and the third characteristic part of the target object,and the second characteristic part of the target object.

In some embodiments, the processor is further configured to, whenestimating the third characteristic part of the target object, estimatethe third characteristic part of the target object based on joint pointinformation of the target object that is determined according to thesecond characteristic part of the target object.

In some embodiments, the processor determines the first characteristicpart of the target object from the one or more first characteristicparts according to the one or more matching parameters corresponding tothe one or more first characteristic parts. Specifically, the processordetermines the largest matching parameter from the one or more matchingparameters corresponding to the one or more first characteristic partsand determines the first characteristic part corresponding to thelargest matching parameter as the first characteristic part of thetarget object.

In some embodiments, the first characteristic part can be a human bodyand the second characteristic part is a preset part of the human body.If the tracking parameters of the target object meet a preset trackingcondition, the processor switches the tracking from tracking the secondcharacteristic part of the target object to tracking the firstcharacteristic part of the target object. Specifically, if the trackingparameters of the target object meet the first preset trackingcondition, the processor switches the tracking from tracking the secondcharacteristic part of the target object to tracking the firstcharacteristic part of the target object.

The tracking parameters of the target object meeting the first presettracking condition can include, for example, a size proportion of thetracking frame of the target object in the captured image being smallerthan or equal to a preset first proportion threshold and/or a distancebetween the target object and the mobile platform being longer than orequal to a preset first distance.

In some embodiments, the first characteristic part can be a preset partof the human body and the second characteristic part is the human body.If the tracking parameters of the target object meet a preset trackingcondition, the processor switches the tracking from tracking the secondcharacteristic part of the target object to tracking the firstcharacteristic part of the target object. Specifically, if the trackingparameters of the target object meet the second preset trackingcondition, the processor switches the tracking from tracking the secondcharacteristic part of the target object to tracking the firstcharacteristic part of the target object.

The tracking parameters of the target object meeting the second presettracking condition can include, for example, the size proportion of thetracking frame of the target object in the captured image being greaterthan or equal to a preset second proportion threshold and/or thedistance between the target object and the mobile platform being shorterthan or equal to a preset second distance.

In some embodiments, the preset part can be a head or a head-shoulderpart.

In some embodiments, the processor can be further configured todetermine the distance between the top edges of the tracking frames ofthe first characteristic part corresponding to the largest matchingparameter and the second characteristic part.

The processor determines the first characteristic part corresponding tothe largest matching parameter as the first characteristic part of thetarget object. Specifically, if the distance is less than or equal tothe preset distance threshold, the processor determines the firstcharacteristic part corresponding to the largest matching parameter asthe first characteristic part of the target object.

In some embodiments, the matching parameter can include at least one ofa coincidence degree matching parameter, an image similarity degreematching parameter, or a geometric similarity degree matching parameter.

An embodiment of the present disclosure further provides a computerreadable storage medium that stores computer commands. When the computercommands are executed (by the processor), the mobile platform can becontrolled according to a method consistent with the disclosure, such asone of the example methods described above.

The system, device, module or unit described in the above embodimentsmay be implemented by a computer chip or entity, or by a product with acertain function. A typical implementation device is a computer, such asa personal computer, a laptop computer, a cellular phone, a smart phone,a personal digital assistant, a media player, a navigation device, anemail sending and receiving device, a game console, a tablet computer, awearable device, or any combination of any of these devices.

For simplification purposes, above devices are divided into variousunits according to their functions when being described. Of course, whenthe present disclosure is implemented, the functions of each unit can beimplemented in the same or multiple software and/or hardware.

Those of ordinary skill in the art should appreciate that embodiments ofthe present disclosure may be provide as a method, a system, or acomputer program product. Therefore, the present disclosure may take theform of an entirely hardware embodiment, an entirely softwareembodiment, or an embodiment combining software and hardware aspects.Moreover, embodiments of the present disclosure may take the form of acomputer program product implemented on one or more computer-usablestorage media (including but is not limited to hard disk storage,CD-ROM, optical memory, etc.) containing computer-executable programcode therein.

The present disclosure is described with reference to flowcharts and/orblock diagrams of methods, devices (systems), and computer programproducts according to embodiments of the present disclosure. Eachprocess and/or block in the flowchart and/or block diagram and acombination of the process and/or block in the flowchart and/or blockdiagram can be implemented by computer program commands. These computerprogram commands can be provided to the processor of general-purposecomputer, special-purpose computer, embedded processor, or otherprogrammable data processing device to produce a machine, so that thecommands generated by the processor of the computer or otherprogrammable data processing device can be used to realize the functionsspecified in one or more flowcharts and/or one or more blocks of theblock diagram.

The computer program commands may also be stored in a computer-readablememory capable of directing a computer or other programmable dataprocessing device to work in a specific manner, so that the commandsstored in the computer-readable memory produce a manufactured articleincluding the commands device that implements the functions specified inone or more workflow in a flowchart, and/or one or more blocks in theblock diagram.

The computer program commands may also be loaded into a computer orother programmable data processing device, so that operating steps insequence can be performed on them to produce a computer-implementedprocess that implements the functions specified in one or more workflowin a flowchart, and/or one or more blocks in the block diagram.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theembodiments disclosed herein. It is intended that the specification andexamples be considered as example only and not to limit the scope of thedisclosure, with a true scope and spirit of the invention beingindicated by the following claims.

What is claimed is:
 1. A control method for a mobile platformcomprising: obtaining a captured image; determining a target firstcharacteristic part of a target object from the captured image;determining a second characteristic part of the target object in thecaptured image; and switching from tracking the second characteristicpart to tracking the target first characteristic part in response to atracking parameter of the target object meeting a preset trackingcondition.
 2. The method of claim 1, wherein determining the targetfirst characteristic part includes: identifying one or more candidatefirst characteristic parts from the captured image; determining one ormore matching parameters each corresponding to one of the one or morecandidate first characteristic parts based on the one or more candidatefirst characteristic parts and the second characteristic part; anddetermining the target first characteristic part of the target objectfrom the one or more candidate first characteristic parts based on theone or more matching parameters.
 3. The method of claim 2, whereindetermining the one or more matching parameters each corresponding toone of the one or more candidate first characteristic parts includes:for each of the one or more matching parameters, determining thematching parameter between the second characteristic part and thecorresponding candidate first characteristic part based on the secondcharacteristic part and the corresponding candidate first characteristicpart.
 4. The method of claim 2, wherein determining the one or morematching parameters each corresponding to one of the one or morecandidate first characteristic parts includes: estimating a thirdcharacteristic part of the target object based on the secondcharacteristic part; and determining one or more matching parametersbetween the third characteristic part and the one or more candidatefirst characteristic parts as the one or more matching parameterscorresponding to the one or more candidate first characteristic parts.5. The method of claim 4, wherein estimating the third characteristicpart includes estimating the third characteristic part based on thesecond characteristic part and a proportional relationship between thesecond characteristic part and the third characteristic part.
 6. Themethod of claim 4, wherein estimating the third characteristic partincludes: determining joint point information of the target objectaccording to the second characteristic part; and estimating the thirdcharacteristic part based on the joint point information.
 7. The methodof claim 2, wherein determining the target first characteristic partfurther includes: determining a largest matching parameter among the oneor more matching parameters; and determining one of the one or morecandidate first characteristic parts corresponding to the largestmatching parameter as the target first characteristic part.
 8. Themethod of claim 7, further comprising: determining a distance between atop edge of a tracking frame of the one of the one or more candidatefirst characteristic parts corresponding to the largest matchingparameter and a top edge of a tracking frame of the secondcharacteristic part; and determining the one of the one or morecandidate first characteristic parts corresponding to the largestmatching parameter as the target first characteristic part includesdetermining the one of the one or more candidate first characteristicparts corresponding to the largest matching parameter as the targetfirst characteristic part in response to the distance being less than orequal to a preset distance threshold.
 9. The method of claim 2, whereinthe one or more matching parameters include at least one of acoincidence degree, an image similarity degree, or a geometricsimilarity degree.
 10. The method of claim 1, wherein: the target firstcharacteristic part includes a target human body and the secondcharacteristic part includes a preset part of the target human body; andswitching from tracking the second characteristic part to tracking thetarget first characteristic part includes switching from tracking thepreset part of the target human body to tracking the target human body.11. The method of claim 10, wherein the preset tracking conditionincludes at least one of: a size proportion of a tracking frame of thetarget object in the captured image being smaller than or equal to apreset proportion threshold, or a distance between the target object andthe mobile platform being larger than or equal to a preset distancethreshold.
 12. The method of claim 1, wherein: the target firstcharacteristic part includes a preset part of a target human body andthe second characteristic part includes the target human body; andswitching from tracking the second characteristic part to tracking thetarget first characteristic part includes switching from tracking thetarget human body to tracking the preset part of the target human body.13. The method of claim 12, wherein the preset tracking conditionincludes at least one of: a size proportion of a tracking frame of thetarget object in the captured image being greater than or equal to apreset proportion threshold, or a distance between the target object andthe mobile platform being less than or equal to a preset distancethreshold.
 14. The method of claim 12, wherein the preset part includesa head or a head-shoulder part.
 15. A mobile platform comprising: amemory storing a computer program; and a processor configured to executethe computer program to: obtain a captured image; determine a targetfirst characteristic part of a target object from the captured image;determine a second characteristic part of the target object in thecaptured image; and switch from tracking the second characteristic partto tracking the target first characteristic part in response to atracking parameter of the target object meeting a preset trackingcondition.
 16. The mobile platform of claim 15, wherein the processor isfurther configured to execute the computer program to: identify one ormore candidate first characteristic parts from the captured image;determine one or more matching parameters each corresponding to one ofthe one or more candidate first characteristic parts based on the one ormore candidate first characteristic parts and the second characteristicpart; and determine the target first characteristic part of the targetobject from the one or more candidate first characteristic parts basedon the one or more matching parameters
 17. The mobile platform of claim16, wherein the processor is further configured to execute the computerprogram to: determine a largest matching parameter among the one or morematching parameters; and determine one of the one or more candidatefirst characteristic parts corresponding to the largest matchingparameter as the target first characteristic part.
 18. The mobileplatform of claim 17, wherein the processor is further configured toexecute the computer program to: determine a distance between a top edgeof a tracking frame of the one of the one or more candidate firstcharacteristic parts corresponding to the largest matching parameter anda top edge of a tracking frame of the second characteristic part; anddetermine the one of the one or more candidate first characteristicparts corresponding to the largest matching parameter as the targetfirst characteristic part in response to the distance being less than orequal to a preset distance threshold.
 19. The mobile platform of claim16, wherein: the target first characteristic part includes a targethuman body and the second characteristic part includes a preset part ofthe target human body; and the processor is further configured toexecute the computer program to switch from tracking the preset part ofthe target human body to tracking the target human body.
 20. The mobileplatform of claim 19, wherein the preset tracking condition includes atleast one of: a size proportion of a tracking frame of the target objectin the captured image being smaller than or equal to a preset proportionthreshold, or a distance between the target object and the mobileplatform being larger than or equal to a preset distance threshold.